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Project Description

This library provides support for computing 1D, 2D and 3D dual-tree complex wavelet
transforms and their inverse in Python.
Full documentation is available online.

Installation

The easiest way to install dtcwt is via easy_install or pip:

$ pip install dtcwt

If you want to check out the latest in-development version, look at
the project’s GitHub page. Once checked out,
installation is based on setuptools and follows the usual conventions for a
Python project:

$ python setup.py install

(Although the develop command may be more useful if you intend to perform any
significant modification to the library.) A test suite is provided so that you
may verify the code works on your system:

$ python setup.py nosetests

This will also write test-coverage information to the cover/ directory.

Further documentation

There is more documentation
available online and you can build your own copy via the Sphinx documentation
system:

$ python setup.py build_sphinx

Compiled documentation may be found in build/docs/html/.

Provenance

Based on the Dual-Tree Complex Wavelet Transform Pack for MATLAB by Nick
Kingsbury, Cambridge University. The original README can be found in
ORIGINAL_README.txt. This file outlines the conditions of use of the original
MATLAB toolbox.

Changes

0.10.1

Fix regression in dtcwt-based image registration.

Allow levels used for dtcwt-based image registration to be customised.